A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data

Rolling bearing fault diagnosis is closely related to the safety of mechanical system. In real-world diagnosis, it is difficult to obtain abundant labeled data due to varying operation conditions, complex working environment and inevitable indirect measurement, which will affect the ability of diagnosing. To tackle this problem, a deep transfer maximum classifier discrepancy method is proposed under few labeled data, which utilizes fully deep learning and transfer learning. Firstly, a batch-normalized long-short term memory (BNLSTM) model which can learn the mapping relationship between two kinds of datasets is designed to generate some auxiliary samples. Then, a transfer maximum classifier discrepancy (TMCD) method, which considers the characteristics of each data type by an adversarial strategy, is applied to align probability distributions of auxiliary samples generated by BNLSTM and unlabeled data from target domain. Sufficient experimental results indicate the effectiveness of the proposed method under few labeled data.